How Do You Respond to Risk? The Use Case Approach

As the number of internet-connected devices skyrockets into the billions, a data security strategy is an increasingly important part of any organization’s ability to manage and protect critical information. Enterprises are migrating to the cloud in droves, however, protecting data in the cloud remains a challenge as employees push to access cloud apps from any device, anywhere. In the last year alone, 1 in 3 organizations were hacked more than 5 times, and with the increased number of attacks the financial cost of security incidents is also rising.

In many cases, breaches are caused by a combination of benevolent insiders, targeted attacks, and malicious insiders. For example, targeted attacks are often enabled inadvertently by well-meaning insiders who fail to comply with data or security policies, which can lead to a data breach. In this webinar, our panel will discuss major trends impacting cyber security – from the rising frequency of attacks and types of threats that organizations should be concerned about the most, and they will adress the risks, priorities, and capabilities that are top of mind for enterprises as they migrate to the cloud.

Over 90% of the world’s data has been generated in the last few years. Accompanying this rapid growth in data comes exponential risks, as witnessed by the spike in cyber attacks of which no organization seems immune. The financial rewards gained by the perpetuators of cyber attacks is blatant, and this is driving continued attacks on companies containing massive amounts of consumer data. For these companies securing data is only half the battle. The risks can be greater when data is transmitted externally, hence it is critical that organizations know where sensitive data is going, how it is being transmitted, and how it is being handled and stored.

On this webinar our panel of experts will discuss some of the best practices organizations can consider to reduce the risk of suffering from a data breach, and to proactively prepare for any subsequent breach that could happen.

Fraud detection is a classic adversarial analytics challenge: As soon as an automated system successfully learns to stop one scheme, fraudsters move on to attack another way. Each scheme requires looking for different signals (i.e. features) to catch; is relatively rare (one in millions for finance or e-commerce); and may take months to investigate a single case (in healthcare or tax, for example) – making quality training data scarce.

This talk will cover a code walk-through, the key lessons learned while building such real-world software systems over the past few years. We'll look for fraud signals in public email datasets, using IPython and popular open-source libraries (scikit-learn, statsmodel, nltk, etc.) for data science and Apache Spark as the compute engine for scalable parallel processing.

David will iteratively build a machine-learned hybrid model – combining features from different data sources and algorithmic approaches, to catch diverse aspects of suspect behavior:

- Natural language processing: finding keywords in relevant context within unstructured text
- Statistical NLP: sentiment analysis via supervised machine learning
- Time series analysis: understanding daily/weekly cycles and changes in habitual behavior
- Graph analysis: finding actions outside the usual or expected network of people
- Heuristic rules: finding suspect actions based on past schemes or external datasets
- Topic modeling: highlighting use of keywords outside an expected context
- Anomaly detection: Fully unsupervised ranking of unusual behavior

Apache Spark is used to run these models at scale – in batch mode for model training and with Spark Streaming for production use. We’ll discuss the data model, computation, and feedback workflows, as well as some tools and libraries built on top of the open-source components to enable faster experimentation, optimization, and productization of the models.